def main():
distance_funcs = {
'canberra': Distances.canberra_distance,
'minkowski': Distances.minkowski_distance,
'euclidean': Distances.euclidean_distance,
'gaussian': Distances.gaussian_kernel_distance,
'inner_prod': Distances.inner_product_distance,
'cosine_dist': Distances.cosine_similarity_distance,
}
scaling_classes = {
'min_max_scale': MinMaxScaler,
'normalize': NormalizationScaler,
}
x_train, y_train, x_val, y_val, x_test, y_test = data_processing()
print('x_train shape = ', x_train.shape)
print('y_train shape = ', y_train.shape)
tuner_without_scaling_obj = HyperparameterTuner()
tuner_without_scaling_obj.tuning_without_scaling(distance_funcs, x_train, y_train, x_val, y_val)
print("**Without Scaling**")
print("k =", tuner_without_scaling_obj.best_k)
print("distance function =", tuner_without_scaling_obj.best_distance_function)
tuner_with_scaling_obj = HyperparameterTuner()
tuner_with_scaling_obj.tuning_with_scaling(distance_funcs, scaling_classes, x_train, y_train, x_val, y_val)
print("\n**With Scaling**")
print("k =", tuner_with_scaling_obj.best_k)
print("distance function =", tuner_with_scaling_obj.best_distance_function)
print("scaler =", tuner_with_scaling_obj.best_scaler)
def main():
distance_funcs = {
'euclidean': Distances.euclidean_distance,
'minkowski': Distances.minkowski_distance,
'gaussian': Distances.gaussian_kernel_distance,
'inner_prod': Distances.inner_product_distance,
'cosine_dist': Distances.cosine_similarity_distance,
}
scaling_classes = {
'min_max_scale': MinMaxScaler,
'normalize': NormalizationScaler,
}
x_train, y_train, x_val, y_val, x_test, y_test = data_processing()
print('x_train shape = ', x_train.shape)
print('y_train shape = ', y_train.shape)
print('x_val shape = ', x_val.shape)
print('y_val shape = ', y_val.shape)
print('x_test shape = ', x_test.shape)
print('y_test shape = ', y_test.shape)
tuner_without_scaling_obj = HyperparameterTuner()
tuner_without_scaling_obj.tuning_without_scaling(distance_funcs, x_train, y_train, x_val, y_val)
print("**Without Scaling**")
print("k =", tuner_without_scaling_obj.best_k)
print("distance function =", tuner_without_scaling_obj.best_distance_function)
print("f1_score=", tuner_without_scaling_obj.best_f1_score)
pred = tuner_without_scaling_obj.best_model.predict(x_test)
correct = 0
for i in range(len(pred)):
if pred[i] == y_test[i]:
correct += 1
accuracy = float(correct)/len(pred)
print ("Accuracy is: ", accuracy)
print ("F1 Score: ", f1_score(y_test, pred))
tuner_with_scaling_obj = HyperparameterTuner()
tuner_with_scaling_obj.tuning_with_scaling(distance_funcs, scaling_classes, x_train, y_train, x_val, y_val)
#
print("\n**With Scaling**")
print("k =", tuner_with_scaling_obj.best_k)
print("distance function =", tuner_with_scaling_obj.best_distance_function)
print("scaler =", tuner_with_scaling_obj.best_scaler)
print("f1_score=", tuner_with_scaling_obj.best_f1_score)
pred_2 = tuner_with_scaling_obj.best_model.predict(x_test)
correct_2 = 0
for i in range(len(pred_2)):
if pred_2[i] == y_test[i]:
correct_2 += 1
accuracy_2 = float(correct_2)/len(pred_2)
print ("Accuracy is: ", accuracy_2)
print ("F1 Score:", f1_score(y_test, pred_2))